python入门教程 - 滑块实战【附源码】

水深无声 2023-09-30 03:19 6阅读 0赞

`文末源码，阅读大约2.8分钟`

**傻瓜式教程 - 体验滑块，提供练习场景及源码。**

--------------------

#### 文章目录 ####

*  环境安装
 *  效果展示
 *  源码

![image][]

## 环境安装 ##

安装python需要的依赖包

> cv2 安装可以参考这里：https://javapub.blog.csdn.net/article/details/123656345

安装webdriver -> chrome

下载对应版本，放在本地 D:\\anaconda3\\Scripts 目录下

> https://registry.npmmirror.com/binary.html?path=chromedriver

--------------------

## 效果展示 ##

GIF效果：https://tva2.sinaimg.cn/large/007F3CC8ly1h0ku3yh9g5g31ex0pfwus.gif

![动画][007F3CC8ly1h0ku3yh9g5g31ex0pfwus.gif]

cv2使用参考：https://blog.csdn.net/RNG\_uzi\_/article/details/90034485

注意：测试时慢点刷，容易封IP。

## 源码 ##

有问题可以留言探讨，公众号：JavaPub

对源码加了大量注释

> 测试网站：http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303

import os
    import cv2
    import time
    import random
    import requests
    import numpy as np
    from PIL import Image
    from io import BytesIO
    from selenium import webdriver
    from selenium.webdriver.common.by import By
    from selenium.webdriver import ActionChains
    from selenium.webdriver.support.wait import WebDriverWait
    from selenium.webdriver.support import expected_conditions as EC
    
    class CrackSlider():
        def __init__(self):
            # self.browser = webdriver.Edge()
            self.browser = webdriver.Chrome()
            self.s2 = r'//*[@id="captcha_div"]/div/div[1]/div/div[1]/img[1]'
            self.s3 = r'//*[@id="captcha_div"]/div/div[1]/div/div[1]/img[2]'
            self.url = 'http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303'  # 测试网站
            self.wait = WebDriverWait(self.browser, 20)
            self.browser.get(self.url)
    
        # 保存俩张图片
        def get_img(self, target, template, xp):
            time.sleep(3)
            target_link = self.browser.find_element_by_xpath(self.s2).get_attribute("src")
            template_link = self.browser.find_element_by_xpath(self.s3).get_attribute("src")
            target_img = Image.open(BytesIO(requests.get(target_link).content))
            template_img = Image.open(BytesIO(requests.get(template_link).content))
            target_img.save(target)
            template_img.save(template)
            size_loc = target_img.size
            print('size_loc[0]-----\n')
            print(size_loc[0])
            zoom = xp / int(size_loc[0])  # 耦合像素
            print('zoom-----\n')
            print(zoom)
            return zoom
    
        def change_size(self, file):
            image = cv2.imread(file, 1)  # 读取图片 image_name应该是变量
            img = cv2.medianBlur(image, 5)  # 中值滤波，去除黑色边际中可能含有的噪声干扰。去噪。
            b = cv2.threshold(img, 15, 255, cv2.THRESH_BINARY)  # 调整裁剪效果，二值化处理。
            binary_image = b[1]  # 二值图--具有三通道
            binary_image = cv2.cvtColor(binary_image, cv2.COLOR_BGR2GRAY)
            x, y = binary_image.shape
            edges_x = []
            edges_y = []
            for i in range(x):
                for j in range(y):
                    if binary_image[i][j] == 255:
                        edges_x.append(i)
                        edges_y.append(j)
    
            left = min(edges_x)  # 左边界
            right = max(edges_x)  # 右边界
            width = right - left  # 宽度
            bottom = min(edges_y)  # 底部
            top = max(edges_y)  # 顶部
            height = top - bottom  # 高度
            pre1_picture = image[left:left + width, bottom:bottom + height]  # 图片截取
            return pre1_picture  # 返回图片数据
    
        # 匹配比对俩图距离
        def match(self, target, template):
            img_gray = cv2.imread(target, 0)
            img_rgb = self.change_size(template)
            template = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) # 图片格式转换为灰度图片 
            # cv2.imshow('template', template)
            # cv2.waitKey(0)
            res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED) # 匹配模式，匹配图片
            run = 1
    
            # 使用二分法查找阈值的精确值
            L = 0
            R = 1
            while run < 20:
                run += 1
                threshold = (R + L) / 2
                if threshold < 0:
                    print('Error')
                    return None
                loc = np.where(res >= threshold)
                if len(loc[1]) > 1:
                    L += (R - L) / 2
                elif len(loc[1]) == 1:
                    break
                elif len(loc[1]) < 1:
                    R -= (R - L) / 2
            res = loc[1][0]
            print('match distance-----\n')
            print(res)
            return res
    
        def move_to_gap(self, tracks):
            slider = self.wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'yidun_slider')))
            ActionChains(self.browser).click_and_hold(slider).perform()
            #element = self.browser.find_element_by_xpath(self.s3)
            #ActionChains(self.browser).click_and_hold(on_element=element).perform()
            while tracks:
                x = tracks.pop(0)
                print('tracks.pop(0)-----\n')
                print(x)
                ActionChains(self.browser).move_by_offset(xoffset=x, yoffset=0).perform()
                #ActionChains(self.browser).move_to_element_with_offset(to_element=element, xoffset=x, yoffset=0).perform()
                #time.sleep(0.01)
            time.sleep(0.05)
            ActionChains(self.browser).release().perform()
    
        def move_to_gap1(self, distance):
            distance += 46
            time.sleep(1)
            element = self.browser.find_element_by_xpath(self.s3)
            ActionChains(self.browser).click_and_hold(on_element=element).perform()
            ActionChains(self.browser).move_to_element_with_offset(to_element=element, xoffset=distance, yoffset=0).perform()
            #ActionChains(self.browser).release().perform()
            time.sleep(1.38)
            ActionChains(self.browser).release(on_element=element).perform()
    
        def move_to_gap2(self, distance):
            element = self.browser.find_elements_by_class_name("yidun_slider")[0]
            action = ActionChains(self.browser)
            mouse_action = action.click_and_hold(on_element=element)
            distance += 11
            distance = int(distance * 32/33)
            move_steps = int(distance/4)
            for i in range(0,move_steps):
                mouse_action.move_by_offset(4,random.randint(-5,5)).perform()
            time.sleep(0.1)
            mouse_action.release().perform()    
    
        # 计算出先加速、后加速的数组
        def get_tracks(self, distance, seconds, ease_func):
            distance += 20
            tracks = [0]
            offsets = [0]
            for t in np.arange(0.0, seconds, 0.1):
                ease = ease_func
                print('ease-----\n')
                print(ease)
                offset = round(ease(t / seconds) * distance)
                print('offset-----\n')
                print(offset)
                tracks.append(offset - offsets[-1])
                print('offset - offsets[-1]-----\n')
                print(offset - offsets[-1])
                offsets.append(offset)
                print('offsets-----\n')
                print(offsets)
            tracks.extend([-3, -2, -3, -2, -2, -2, -2, -1, -0, -1, -1, -1])
            return tracks
        def get_tracks1(self,distance):
            """
            根据偏移量获取移动轨迹
            :param distance: 偏移量
            :return: 移动轨迹
            """
            # 移动轨迹
            track = []
            # 当前位移
            current = 0
            # 减速阈值
            mid = distance * 4 / 5
            # 计算间隔
            t = 0.2
            # 初速度
            v = 0
    
            while current < distance:
                if current < mid:
                    # 加速度为正 2
                    a = 4
                else:
                    # 加速度为负 3
                    a = -3
                # 初速度 v0
                v0 = v
                # 当前速度 v = v0 + at
                v = v0 + a * t
                # 移动距离 x = v0t + 1/2 * a * t^2
                move = v0 * t + 1 / 2 * a * t * t
                # 当前位移
                current += move
                # 加入轨迹
                track.append(round(move))
            return track
    
        def ease_out_quart(self, x):
            res = 1 - pow(1 - x, 4)
            print('ease_out_quart-----\n')
            print(res)
            return res
    
    # 发生意外，请留言。https://javapub.blog.csdn.net/article/details/123730597
    if __name__ == '__main__':
        xp = 320  # 验证码的像素-长
        target = 'target.jpg'  # 临时保存的图片名
        template = 'template.png'  # 临时保存的图片名
    
        cs = CrackSlider()
        zoom = cs.get_img(target, template, xp)
        distance = cs.match(target, template)
        track = cs.get_tracks((distance + 7) * zoom, random.randint(2, 4), cs.ease_out_quart)
        #track = cs.get_tracks1(distance)
        #track = cs.get_tracks((distance + 7) * zoom, random.randint(1, 2), cs.ease_out_quart)
        cs.move_to_gap(track)
        #cs.move_to_gap1(distance)
        #cs.move_to_gap2(distance)
        time.sleep(2)
        #cs.browser.close()

**同名公众号，更多工具解决方案**

![image][image 1]

[image]: https://img-blog.csdnimg.cn/img_convert/a58e77dd526ea8ab16ac3f876eb1260e.png
[007F3CC8ly1h0ku3yh9g5g31ex0pfwus.gif]: https://tva2.sinaimg.cn/large/007F3CC8ly1h0ku3yh9g5g31ex0pfwus.gif
[image 1]: https://img-blog.csdnimg.cn/img_convert/1d348d42a558dd8a07e92c692f737c40.png